Polar-Grid Representation and Kriging-Based 2.5D Interpolation for Urban Environment Modelling

In this paper a spatial interpolation approach, based on polar-grid representation and Kriging predictor, is proposed for 3D point cloud sampling. Discrete grid representation is a widely used technique because of its simplicity and capacity of providing an efficient and compact representation, allowing subsequent applications such as artificial perception and autonomous navigation. Two-dimensional occupancy grid representations have been studied extensively in the past two decades, and recently 2.5D and 3D grid-based approaches dominate current applications. A key challenge in perception systems for vehicular applications is to balance low computational complexity and reliable data interpretation. To this end, this paper contributes with a discrete 2.5D polar-grid that upsamples the input data, ie sparse 3D point cloud, by means of a deformable kriging-based interpolation strategy. Experiments carried out on the KITTI dataset, using data from a LIDAR, demonstrate that the approach proposed in this work allows a proper representation of urban environments.